Author(s)
Quinn C1, Garrison LP2, Briggs A3, Pownell A4, Atkins M5, de Pouvourville G6, Harrington K7, Ascierto PA8, McEwan P9, Doan J10, Wagner S10, Borrill J11, Wu E12
1PRMA Consulting Ltd., Oxford, OXF, UK, 2University of Washington, Seattle, WA, USA, 3University of Glasgow, Glasgow, UK, 4PRMA Consulting, Liverpool, CHW, UK, 5Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA, 6ESSEC Business School, Cergy-Pontoise, France, 7The Royal Marsden/Institute of Cancer Research NIHR Biomedical Research Centre, London, UK, 8Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy, 9Health Economics and Outcomes Research Ltd, Cardiff, UK, 10Bristol-Myers Squibb, Princeton, NJ, USA, 11Bristol-Myers Squibb, Uxbridge, LON, UK, 12Bristol-Myers Squibb, Lawrenceville, NJ, USA
Background Immuno-oncology drugs (IO) target a patient’s immune system rather than directly attacking the tumour. This leads to elimination of the last tumour cells, providing durable responses and long-term survival (LTS) for some, but not all, patients, which can persist long after treatment has ceased. Standard approaches to survival analysis used by health technology assessment (HTA) bodies may not fully reflect the value of these drugs. Objective The aim of this research was to detail the key challenges in demonstrating the value of LTS in patients treated with IO that manufacturers have encountered at HTA. Methodology A targeted literature review was used to inform a discussion paper on the challenges in demonstrating the LTS benefit of IO. An international, multi-stakeholder steering committee (SC) comprising payers, economists, and clinicians from the US, UK, France, Italy, and Sweden refined the document. The issues were scrutinized further and finalized via double-blinded, country-level, multi-stakeholder panels in the US, UK, France, Germany, and Sweden. Results Overall, the SC and country-level panels observed that basing HTAs on standard parametric survival models to estimate LTS is likely to underestimate the impact of IO treatment on survival outcomes, if IO treatment generates a plateau in the survival curve. The 3 key challenges identified for IO when demonstrating survival benefit were: (1) lack of a model structure that fully captures how IO therapy affects the course of disease; (2) immature data available at the time of the HTA submission; and (3) survival analysis and extrapolation. Conclusion Multiple challenges remain in capturing the full clinical benefit of IO in HTA submissions. There persists a need to reduce uncertainty around estimates of LTS. Further research is encouraged to develop and validate both IO models and appropriate surrogates for LTS.
Conference/Value in Health Info
2019-11, ISPOR Europe 2019, Copenhagen, Denmark
Code
PCN57
Topic
Clinical Outcomes, Health Technology Assessment, Methodological & Statistical Research
Topic Subcategory
Decision & Deliberative Processes, Modeling and simulation, Relating Intermediate to Long-term Outcomes
Disease
No Specific Disease, Oncology